To effectively measure the impact of mutants and real bug fixes on the training process and to
exclude variances due to implementation details, we implemented RealiT  together with several baselines considered during our evaluation in a unified frameworkhttps://github.com/cedricrupb/nbfbaselines. In particular, we followed the design of Hellendoorn et al. [1]} by implementing a common localization and repair framework with exchangeable components (e.g. token encoder, localization and/or repair modules). In the process, we reimplemented or reused state-of-the-art components for all employed subcomponents. For example, RealiT  and all Transformer-based baselines are built upon
the official BERT implementation from the transformer library [2]}. The localization and repair modules together with the graph-based baselines are implemented closely to the implementation of the PyBugsLab model [3]}.
In addition, we reimplemented the code preprocessing pipelines for tokenization [1]} and graph construction [3]} (used for the graph-based baselines) in independent libraries to facilitate reuse.
Finally, we plan to release all trained checkpoints of RealiT  and all evaluated models. We think that these are not only valuable for reproducing our results but also provide easy access to effective models in neural bug localization and repair.
